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Applications in Music and Audio Processing (MLR-MUSI)

Audio-based Detection of Explicit Content in Music


We present a novel automatic system for performing explicit content detection directly on the audio signal. Our modular approach uses an audio-to-character recognition model, a keyword spotting model associated with a dictionary of carefully chosen keywords, and a Random Forest classification model for the final decision. To the best of our knowledge, this is the first explicit content detection system based on audio only. We demonstrate the individual relevance of our modules on a set of sub-tasks and compare our approach to a lyrics-informed oracle and an end-to-end naive architecture.

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Authors:
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc
Submitted On:
27 May 2020 - 6:05am
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VAGLIO_ICASSP.pdf

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[1] Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, "Audio-based Detection of Explicit Content in Music", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5441. Accessed: Sep. 25, 2020.
@article{5441-20,
url = {http://sigport.org/5441},
author = {Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc },
publisher = {IEEE SigPort},
title = {Audio-based Detection of Explicit Content in Music},
year = {2020} }
TY - EJOUR
T1 - Audio-based Detection of Explicit Content in Music
AU - Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5441
ER -
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). Audio-based Detection of Explicit Content in Music. IEEE SigPort. http://sigport.org/5441
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, 2020. Audio-based Detection of Explicit Content in Music. Available at: http://sigport.org/5441.
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). "Audio-based Detection of Explicit Content in Music." Web.
1. Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. Audio-based Detection of Explicit Content in Music [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5441

Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction


Deep neural networks (DNNs) are successful in applications with matching inference and training distributions. In realworld scenarios, DNNs have to cope with truly new data samples during inference, potentially coming from a shifted data distribution. This usually causes a drop in performance. Acoustic scene classification (ASC) with different recording devices is one of this situation. Furthermore, an imbalance in quality and amount of data recorded by different devices causes severe challenges. In this paper, we introduce two calibration methods to tackle these challenges.

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Authors:
Truc Nguyen, Franz Pernkopf, Michal Kosmider
Submitted On:
13 May 2020 - 5:52pm
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ICASSP2020_TrucNguyen_slides.pdf

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[1] Truc Nguyen, Franz Pernkopf, Michal Kosmider, "Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5158. Accessed: Sep. 25, 2020.
@article{5158-20,
url = {http://sigport.org/5158},
author = {Truc Nguyen; Franz Pernkopf; Michal Kosmider },
publisher = {IEEE SigPort},
title = {Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction},
year = {2020} }
TY - EJOUR
T1 - Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction
AU - Truc Nguyen; Franz Pernkopf; Michal Kosmider
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5158
ER -
Truc Nguyen, Franz Pernkopf, Michal Kosmider. (2020). Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction. IEEE SigPort. http://sigport.org/5158
Truc Nguyen, Franz Pernkopf, Michal Kosmider, 2020. Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction. Available at: http://sigport.org/5158.
Truc Nguyen, Franz Pernkopf, Michal Kosmider. (2020). "Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction." Web.
1. Truc Nguyen, Franz Pernkopf, Michal Kosmider. Acoustic Scene Classification for Mismatched Recording Devices Using Heated-Up Softmax and Spectrum Correction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5158

RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES


In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported end-to-end deep learning based approaches work well in localizing a single source directly from multi-channel raw-audio, but are not easily extendable to localize multiple sources due to the well known permutation problem.

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Authors:
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang
Submitted On:
3 May 2020 - 3:51pm
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Raw Waveform based MSL

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[1] Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5118. Accessed: Sep. 25, 2020.
@article{5118-20,
url = {http://sigport.org/5118},
author = {Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang },
publisher = {IEEE SigPort},
title = {RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES},
year = {2020} }
TY - EJOUR
T1 - RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES
AU - Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5118
ER -
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. IEEE SigPort. http://sigport.org/5118
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, 2020. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. Available at: http://sigport.org/5118.
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES." Web.
1. Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5118

VaPar Synth - A Variational Parametric Model for Audio Synthesis


With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to model audio signals directly in the time or frequency domain. In the interest of more flexible control over the generated sound, it could be more useful to work with a parametric representation of the signal which corresponds more directly to the musical attributes such as pitch, dynamics and timbre.

Paper Details

Authors:
Krishna Subramani, Preeti Rao, Alexandre D'Hooge
Submitted On:
18 April 2020 - 2:10am
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Presentation Slides

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[1] Krishna Subramani, Preeti Rao, Alexandre D'Hooge, "VaPar Synth - A Variational Parametric Model for Audio Synthesis", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5104. Accessed: Sep. 25, 2020.
@article{5104-20,
url = {http://sigport.org/5104},
author = {Krishna Subramani; Preeti Rao; Alexandre D'Hooge },
publisher = {IEEE SigPort},
title = {VaPar Synth - A Variational Parametric Model for Audio Synthesis},
year = {2020} }
TY - EJOUR
T1 - VaPar Synth - A Variational Parametric Model for Audio Synthesis
AU - Krishna Subramani; Preeti Rao; Alexandre D'Hooge
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5104
ER -
Krishna Subramani, Preeti Rao, Alexandre D'Hooge. (2020). VaPar Synth - A Variational Parametric Model for Audio Synthesis. IEEE SigPort. http://sigport.org/5104
Krishna Subramani, Preeti Rao, Alexandre D'Hooge, 2020. VaPar Synth - A Variational Parametric Model for Audio Synthesis. Available at: http://sigport.org/5104.
Krishna Subramani, Preeti Rao, Alexandre D'Hooge. (2020). "VaPar Synth - A Variational Parametric Model for Audio Synthesis." Web.
1. Krishna Subramani, Preeti Rao, Alexandre D'Hooge. VaPar Synth - A Variational Parametric Model for Audio Synthesis [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5104

Source Coding of Audio Signals with a Generative Model


We consider source coding of audio signals with the help of a generative model. We use a construction where a waveform is first quantized, yielding a finite bitrate representation. The waveform is then reconstructed by random sampling from a model conditioned on the quantized waveform. The proposed coding scheme is theoretically analyzed. Using SampleRNN as the generative model, we demonstrate that the proposed coding structure provides performance competitive with state-of-the-art source coding tools for specific categories of audio signals.

Paper Details

Authors:
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou
Submitted On:
14 April 2020 - 5:16am
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SourceCodingOfAudioSignals_ICASSP2020_demo.zip

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[1] Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, "Source Coding of Audio Signals with a Generative Model", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5094. Accessed: Sep. 25, 2020.
@article{5094-20,
url = {http://sigport.org/5094},
author = {Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou },
publisher = {IEEE SigPort},
title = {Source Coding of Audio Signals with a Generative Model},
year = {2020} }
TY - EJOUR
T1 - Source Coding of Audio Signals with a Generative Model
AU - Roy Fejgin; Janusz Klejsa; Lars Villemoes; Cong Zhou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5094
ER -
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). Source Coding of Audio Signals with a Generative Model. IEEE SigPort. http://sigport.org/5094
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou, 2020. Source Coding of Audio Signals with a Generative Model. Available at: http://sigport.org/5094.
Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. (2020). "Source Coding of Audio Signals with a Generative Model." Web.
1. Roy Fejgin, Janusz Klejsa, Lars Villemoes, Cong Zhou. Source Coding of Audio Signals with a Generative Model [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5094

Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method


In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized $L_1$-regularization. Assuming a sparsity prior on the state, the joint state estimation and parameter learning problem is cast as an unconstrained optimization problem. However, when the dimensionality of state or parameters is large, memory requirements and computation of learning algorithms are generally prohibitive.

Paper Details

Authors:
Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä
Submitted On:
11 October 2019 - 11:29am
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mlsp_poster.pdf

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[1] Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä , "Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4853. Accessed: Sep. 25, 2020.
@article{4853-19,
url = {http://sigport.org/4853},
author = {Rui Gao; Filip Tronarp;Zheng Zhao; Simo Särkkä },
publisher = {IEEE SigPort},
title = {Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method},
year = {2019} }
TY - EJOUR
T1 - Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method
AU - Rui Gao; Filip Tronarp;Zheng Zhao; Simo Särkkä
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4853
ER -
Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä . (2019). Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method. IEEE SigPort. http://sigport.org/4853
Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä , 2019. Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method. Available at: http://sigport.org/4853.
Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä . (2019). "Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method." Web.
1. Rui Gao, Filip Tronarp,Zheng Zhao, Simo Särkkä . Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4853

Modeling nonlinear audio effects with end-to-end deep neural networks


Audio processors whose parameters are modified periodically
over time are often referred as time-varying or modulation based
audio effects. Most existing methods for modeling these type of
effect units are often optimized to a very specific circuit and cannot
be efficiently generalized to other time-varying effects. Based on
convolutional and recurrent neural networks, we propose a deep
learning architecture for generic black-box modeling of audio processors
with long-term memory. We explore the capabilities of

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Authors:
Emmanouil Benetos, Joshua D. Reiss
Submitted On:
10 May 2019 - 12:06pm
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ICASSP___Presentation_Martinez_Ramirez.pdf

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[1] Emmanouil Benetos, Joshua D. Reiss, "Modeling nonlinear audio effects with end-to-end deep neural networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4368. Accessed: Sep. 25, 2020.
@article{4368-19,
url = {http://sigport.org/4368},
author = {Emmanouil Benetos; Joshua D. Reiss },
publisher = {IEEE SigPort},
title = {Modeling nonlinear audio effects with end-to-end deep neural networks},
year = {2019} }
TY - EJOUR
T1 - Modeling nonlinear audio effects with end-to-end deep neural networks
AU - Emmanouil Benetos; Joshua D. Reiss
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4368
ER -
Emmanouil Benetos, Joshua D. Reiss. (2019). Modeling nonlinear audio effects with end-to-end deep neural networks. IEEE SigPort. http://sigport.org/4368
Emmanouil Benetos, Joshua D. Reiss, 2019. Modeling nonlinear audio effects with end-to-end deep neural networks. Available at: http://sigport.org/4368.
Emmanouil Benetos, Joshua D. Reiss. (2019). "Modeling nonlinear audio effects with end-to-end deep neural networks." Web.
1. Emmanouil Benetos, Joshua D. Reiss. Modeling nonlinear audio effects with end-to-end deep neural networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4368

ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES


This paper proposes an online approach to the singing voice separation problem. Based on a combination of one-dimensional convolutional layers along the frequency axis and recurrent layers to enforce temporal coherency, state-of-the-art performance is achieved. The concept of using deep features in the loss function to guide training and improve the model’s performance is also investigated.

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Authors:
Clement S. J. Doire
Submitted On:
9 May 2019 - 3:05am
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Poster presentation OR-U-Net

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[1] Clement S. J. Doire, "ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4160. Accessed: Sep. 25, 2020.
@article{4160-19,
url = {http://sigport.org/4160},
author = {Clement S. J. Doire },
publisher = {IEEE SigPort},
title = {ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES},
year = {2019} }
TY - EJOUR
T1 - ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES
AU - Clement S. J. Doire
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4160
ER -
Clement S. J. Doire. (2019). ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES. IEEE SigPort. http://sigport.org/4160
Clement S. J. Doire, 2019. ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES. Available at: http://sigport.org/4160.
Clement S. J. Doire. (2019). "ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES." Web.
1. Clement S. J. Doire. ONLINE SINGING VOICE SEPARATION USING A RECURRENT ONE-DIMENSIONAL U-NET TRAINED WITH DEEP FEATURE LOSSES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4160

TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING


Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out. Songs are human voice carrying plenty of semantic information just as speech, and may be considered as a special type of speech with highly flexible prosody. The various problems in song audio, for example the significantly changing phone duration over highly flexible pitch contours, make the recognition of lyrics from song audio much more difficult. This paper reports an initial attempt towards this goal.

Paper Details

Authors:
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee
Submitted On:
15 April 2018 - 12:49am
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poster_v4.pdf

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[1] Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee, "TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2878. Accessed: Sep. 25, 2020.
@article{2878-18,
url = {http://sigport.org/2878},
author = {Che-Ping Tsai; Yi-Lin Tuan; Lin-shan Lee },
publisher = {IEEE SigPort},
title = {TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING},
year = {2018} }
TY - EJOUR
T1 - TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING
AU - Che-Ping Tsai; Yi-Lin Tuan; Lin-shan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2878
ER -
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee. (2018). TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING. IEEE SigPort. http://sigport.org/2878
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee, 2018. TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING. Available at: http://sigport.org/2878.
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee. (2018). "TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING." Web.
1. Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee. TRANSCRIBING LYRICS FROM COMMERCIAL SONG AUDIO: THE FIRST STEP TOWARDS SINGING CONTENT PROCESSING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2878

Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection

Paper Details

Authors:
Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar
Submitted On:
14 April 2018 - 3:25pm
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ICASSP (2018_04_14).pdf

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[1] Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar, "Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2849. Accessed: Sep. 25, 2020.
@article{2849-18,
url = {http://sigport.org/2849},
author = {Josh Fromm; Matthai Philipose; Ivan Tashev; Shuayb Zarar },
publisher = {IEEE SigPort},
title = {Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection},
year = {2018} }
TY - EJOUR
T1 - Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection
AU - Josh Fromm; Matthai Philipose; Ivan Tashev; Shuayb Zarar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2849
ER -
Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar. (2018). Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection. IEEE SigPort. http://sigport.org/2849
Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar, 2018. Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection. Available at: http://sigport.org/2849.
Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar. (2018). "Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection." Web.
1. Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar. Limiting Numerical Precision of Neural Networks to Achieve Real-time Voice Activity Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2849

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